Lead Python Backend Developer

Software Engineer (Python/FastAPI) — AI Applications

A high number of candidates may make applications for this position, so make sure to send your CV and application through as soon as possible.

• Role: Build, operate, and scale Python APIs and backend services that integrate AI capabilities (e.g., Google Gemini via Vertex AI) to power internal and external applications. You will not build models; you’ll productionize integrations, orchestrations, and data flows around them.

Required Qualifications

• 6–8+ years software engineering (mid–senior); strong Python 3.x.+
• Production FastAPI (or Flask) delivering secure, versioned APIs.
• Cloud experience on GCP or AWS, Azure (e.g., Cloud Run/GKE/Pub/Sub or Lambda/EKS/API Gateway/SQS).
• Datastores: Postgres/MySQL and one of Redis/ElastiCache/Memorystore; migrations and schema design.
• CI/CD (GitHub Actions/Cloud Build), containerization (Docker), IaC familiarity (Terraform helpful).
• Testing culture (unit/integration/contract), API specs (OpenAPI), and observability.
• Strong grasp of resiliency patterns and performance tuning in distributed systems.

Preferred Qualifications

• Experience integrating AI services (Vertex AI/Gemini, AWS Bedrock, OpenAI) and prompt/response handling.
• Intermediate data engineering skills with Python/SQL
• Security: OAuth/OIDC, JWT, service-to-service auth, KMS/Secrets Manager.
• Data pipelines basics (e.g., batch to feature/API), and schema evolution practices.
• Front end experience with React is a big plus (this would ultimately mean a full stack candidate).

What You’ll Do

• Design, implement, and document FastAPI services and SDKs for AI features.
• Integrate third-party AI providers (Gemini, Bedrock, OpenAI) with secure, observable patterns.
• Build event-driven and synchronous backends (REST/webhooks/queues) with robust error handling.
• Optimize latency, throughput, and cost; add caching, rate limiting, and retries/circuit breakers.
• Ship high-quality code with tests, CI/CD.
• Instrument services (structured logs, metrics, traces) and own production readiness/runbooks.
• Collaborate with product, data, and ML teams to translate requirements into APIs and workflows. xywuqvp
• Uphold security, privacy, and compliance (secrets, OAuth/OIDC, PII handling).